The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
WEKA: a machine learning workbench
Abstract
WEKA is a workbench for machine learning that is intended to aid in the application of machine learning techniques to a variety of real-world problems, in particular, those arising from agricultural and horticultural domains. Unlike other machine learning projects, the emphasis is on providing a working environment for the domain specialist rather than the machine learning expert. Lessons learned include the necessity of providing a wealth of interactive tools for data manipulation, result visualization, database linkage, and cross-validation and comparison of rule sets, to complement the basic machine learning tools.>
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Citations
Cites
(1992) · Cited by 23,431
Knowledge acquisition via incremental conceptual clustering
Fisher, D. H. (1987) · Machine Learning · doi:10.1007/bf00114265 · Cited by 1,991
PRISM: An algorithm for inducing modular rules
Cendrowska, J. (1987) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(87)80003-2 · Cited by 650
Quinlan, J. R., & Cameron-Jones, R. M. (1993) · Lecture Notes in Computer Science · doi:10.1007/3-540-56602-3_124 · Cited by 528
AutoClass: A Bayesian Classification System
CHEESEMAN, P., KELLY, J., SELF, M., STUTZ, J., TAYLOR, W., & FREEMAN, D. (1988) · Machine Learning Proceedings 1988 · doi:10.1016/b978-0-934613-64-4.50011-6 · Cited by 498
LISP‐STAT: An Object‐Oriented Environment for Statistical Computing and Dynamic Graphics
Tierney, L. (1990) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316818 · Cited by 282
Concept learning and heuristic classification in weak-theory domains
Porter, B. W., Bareiss, R., & Holte, R. C. (1990) · Artificial Intelligence · doi:10.1016/0004-3702(90)90041-w · Cited by 250
MLC++: a machine learning library in C++
Kohavi, R., John, G. H., Long, R., Manley, D., & Pfleger, K. (2002) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/tai.1994.346412 · Cited by 193
OC1: randomized induction of oblique decision trees
Murthy, S. K., Kasif, S., Salzberg, S. L., & Beigel, R. (1993) · Cited by 124
Encouraging Experimental Results on Learning CNF
Mooney, R. J. (1995) · Machine Learning · doi:10.1007/bf00994661 · Cited by 37
Cited by
(1998) · Cited by 3,476
Automated machine learning: Review of the state-of-the-art and opportunities for healthcare
Waring, J., Lindvall, C., & Umeton, R. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101822 · Cited by 834
Enabling Effective Programming and Flexible Management of Efficient Body Sensor Network Applications
Fortino, G., Giannantonio, R., Gravina, R., Kuryloski, P., & Jafari, R. (2013) · IEEE Transactions on Human-Machine Systems · doi:10.1109/tsmcc.2012.2215852 · Cited by 412
Learning a Metric for Code Readability
Buse, R. P. L., & Weimer, W. R. (2010) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2009.70 · Cited by 396
Lawrence, J., & Reed, C. (2020) · Computational Linguistics · doi:10.1162/coli_a_00364 · Cited by 396
Metadata
- Authors
- Holmes, G., Donkin, A., & Witten, I. H.
- Venue
- Proceedings of ANZIIS '94 - Australian New Zealnd Intelligent Information Systems Conference
- Publisher
- IEEE
- Published
- 2002-12-17
- Pages
- 357-361
- OpenAlex
- W1566376227
- Cited by
- 930
- References
- 11
- FWCI
- 1.79
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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